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Articles de revues sur le sujet « Protein »

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Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 14 septembre 2024

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1

Akhter, Tahmin, S. Kanamaru et F. Arisaka. « 2P043 Protein interactions among neck proteins, gp13/gp14, and the connector protein, gp15, of bacteriophage T4 ». Seibutsu Butsuri 45, supplement (2005) : S130. http://dx.doi.org/10.2142/biophys.45.s130_3.

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2

Cao, Yi, Teri Yoo, Shulin Zhuang et Hongbin Li. « Protein–Protein Interaction Regulates Proteins’ Mechanical Stability ». Journal of Molecular Biology 378, n^o 5 (mai 2008) : 1132–41. http://dx.doi.org/10.1016/j.jmb.2008.03.046.

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3

Nawas, Mariam T., Evan J. Walker, Megan B. Richie, Andrew A. White et Gerald Hsu. « A Protean Protein ». Journal of Hospital Medicine 14, n^o 2 (février 2019) : 117–22. http://dx.doi.org/10.12788/jhm.3102.

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4

Campbell, P. « Proteinâprotein recognition ». Biochemistry and Molecular Biology Education 29, n^o 5 (septembre 2001) : 211–12. http://dx.doi.org/10.1016/s1470-8175(01)00067-4.

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5

Gómez, Antonio, Sergio Hernández, Isaac Amela, Jaume Piñol, Juan Cedano et Enrique Querol. « Do protein–protein interaction databases identify moonlighting proteins ? » Molecular BioSystems 7, n^o 8 (2011) : 2379. http://dx.doi.org/10.1039/c1mb05180f.

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6

Busler, Valerie J., Victor J. Torres, Mark S. McClain, Oscar Tirado, David B. Friedman et Timothy L. Cover. « Protein-Protein Interactions among Helicobacter pylori Cag Proteins ». Journal of Bacteriology 188, n^o 13 (1 juillet 2006) : 4787–800. http://dx.doi.org/10.1128/jb.00066-06.

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ABSTRACT Many Helicobacter pylori isolates contain a 40-kb region of chromosomal DNA known as the cag pathogenicity island (PAI). The risk for development of gastric cancer or peptic ulcer disease is higher among humans infected with cag PAI-positive H. pylori strains than among those infected with cag PAI-negative strains. The cag PAI encodes a type IV secretion system that translocates CagA into gastric epithelial cells. To identify Cag proteins that are expressed by H. pylori during growth in vitro, we compared the proteomes of a wild-type H. pylori strain and an isogenic cag PAI deletion mutant using two-dimensional difference gel electrophoresis (2D-DIGE) in multiple pH ranges. Seven Cag proteins were identified by this approach. We then used a yeast two-hybrid system to detect potential protein-protein interactions among 14 Cag proteins. One heterotypic interaction (CagY/7 with CagX/8) and two homotypic interactions (involving H. pylori VirB11/ATPase and Cag5) were similar to interactions previously reported to occur among homologous components of the Agrobacterium tumefaciens type IV secretion system. Other interactions involved Cag proteins that do not have known homologues in other bacterial species. Biochemical analysis confirmed selected interactions involving five of the proteins that were identified by 2D-DIGE. Protein-protein interactions among Cag proteins are likely to have an important role in the assembly of the H. pylori type IV secretion apparatus.

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7

Kim, J., K. Harter et A. Theologis. « Protein-protein interactions among the Aux/IAA proteins ». Proceedings of the National Academy of Sciences 94, n^o 22 (28 octobre 1997) : 11786–91. http://dx.doi.org/10.1073/pnas.94.22.11786.

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8

Liu, Jun O. « Recruitment of proteins to modulate protein-protein interactions ». Chemistry & ; Biology 6, n^o 8 (août 1999) : R213—R215. http://dx.doi.org/10.1016/s1074-5521(99)80080-5.

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9

Lin, Ya-Ling, Chia-Yi Chen, Ching-Ping Cheng et Long-Sen Chang. « Protein–protein interactions of KChIP proteins and Kv4.2 ». Biochemical and Biophysical Research Communications 321, n^o 3 (août 2004) : 606–10. http://dx.doi.org/10.1016/j.bbrc.2004.07.006.

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10

Lin, Hening, et Virginia W. Cornish. « In Vivo Protein-Protein Interaction Assays : Beyond Proteins ». Angewandte Chemie International Edition 40, n^o 5 (2 mars 2001) : 871–75. http://dx.doi.org/10.1002/1521-3773(20010302)40:5<871 ::aid-anie871>3.0.co;2-s.

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11

Qiu, Jiajun, Michael Bernhofer, Michael Heinzinger, Sofie Kemper, Tomas Norambuena, Francisco Melo et Burkhard Rost. « ProNA2020 predicts protein–DNA, protein–RNA, and protein–protein binding proteins and residues from sequence ». Journal of Molecular Biology 432, n^o 7 (mars 2020) : 2428–43. http://dx.doi.org/10.1016/j.jmb.2020.02.026.

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12

Velesinović, Aleksandar, et Goran Nikolić. « Protein-protein interaction networks and protein-ligand docking : Contemporary insights and future perspectives ». Acta Facultatis Medicae Naissensis 38, n^o 1 (2021) : 5–17. http://dx.doi.org/10.5937/afmnai38-28322.

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Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry - protein-ligand docking and the prediction of protein-protein interaction networks.

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Sharif, Shahin Behrouz, Nina Zamani et Brian P. Chadwick. « BAZ1B the Protean Protein ». Genes 12, n^o 10 (28 septembre 2021) : 1541. http://dx.doi.org/10.3390/genes12101541.

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The bromodomain adjacent to the zinc finger domain 1B (BAZ1B) or Williams syndrome transcription factor (WSTF) are just two of the names referring the same protein that is encoded by the WBSCR9 gene and is among the 26–28 genes that are lost from one copy of 7q11.23 in Williams syndrome (WS: OMIM 194050). Patients afflicted by this contiguous gene deletion disorder present with a range of symptoms including cardiovascular complications, developmental defects as well as a characteristic cognitive and behavioral profile. Studies in patients with atypical deletions and mouse models support BAZ1B hemizygosity as a contributing factor to some of the phenotypes. Focused analysis on BAZ1B has revealed this to be a versatile nuclear protein with a central role in chromatin remodeling through two distinct complexes as well as being involved in the replication and repair of DNA, transcriptional processes involving RNA Polymerases I, II, and III as well as possessing kinase activity. Here, we provide a comprehensive review to summarize the many aspects of BAZ1B function including its recent link to cancer.

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14

Requena, Jesús R. « The protean prion protein ». PLOS Biology 18, n^o 6 (25 juin 2020) : e3000754. http://dx.doi.org/10.1371/journal.pbio.3000754.

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15

Acuner Ozbabacan, S. E., H. B. Engin, A. Gursoy et O. Keskin. « Transient protein-protein interactions ». Protein Engineering Design and Selection 24, n^o 9 (15 juin 2011) : 635–48. http://dx.doi.org/10.1093/protein/gzr025.

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16

Kukar, Thomas, Sarah Eckenrode, Yunrong Gu, Wei Lian, Mike Megginson, Jin-Xiong She et Donghai Wu. « Protein Microarrays to Detect Protein–Protein Interactions Using Red and Green Fluorescent Proteins ». Analytical Biochemistry 306, n^o 1 (juillet 2002) : 50–54. http://dx.doi.org/10.1006/abio.2002.5614.

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17

Ryu, Jae-Woon, Tae-Ho Kang, Jae-Soo Yoo et Hak-Yong Kim. « Analysis of Essential Proteins in Protein-Protein Interaction Networks ». Journal of the Korea Contents Association 8, n^o 6 (28 juin 2008) : 74–81. http://dx.doi.org/10.5392/jkca.2008.8.6.074.

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18

Burbelo, Peter D., Adam E. Kisailus et Jeremy W. Peck. « Detecting Protein-Protein Interactions Using Renilla Luciferase Fusion Proteins ». BioTechniques 33, n^o 5 (novembre 2002) : 1044–50. http://dx.doi.org/10.2144/02335st05.

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19

Dong, Yun Yuan, et Xian Chun Zhang. « Nonessential-Nonhub Proteins in the Protein-Protein Interaction Network ». Advanced Materials Research 934 (mai 2014) : 159–64. http://dx.doi.org/10.4028/www.scientific.net/amr.934.159.

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Protein-protein interaction (PPI) networks provide a simplified overview of the web of interactions that take place inside a cell. According to the centrality-lethality rule, hub proteins (proteins with high degree) tend to be essential in the PPI network. Moreover, there are also many low degree proteins in the PPI network, but they have different lethality. Some of them are essential proteins (essential-nonhub proteins), and the others are not (nonessential-nonhub proteins). In order to explain why nonessential-nonhub proteins don’t have essentiality, we propose a new measure n-iep (the number of essential neighbors) and compare nonessential-nonhub proteins with essential-nonhub proteins from topological, evolutionary and functional view. The comparison results show that there are statistical differences between nonessential-nonhub proteins and essential-nonhub proteins in centrality measures, clustering coefficient, evolutionary rate and the number of essential neighbors. These are reasons why nonessential-nonhub proteins don’t have lethality.

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20

Cheng, Miaomiao, Lizhen Liu, Hanshi Wang, Chao Du et Wei Song. « Essential Proteins Discovery from Weighted Protein–Protein Interaction Networks ». Journal of Bionanoscience 8, n^o 4 (1 août 2014) : 293–97. http://dx.doi.org/10.1166/jbns.2014.1239.

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21

Dimitrova, Maria, Isabelle Imbert, Marie Paule Kieny et Catherine Schuster. « Protein-Protein Interactions between Hepatitis C Virus Nonstructural Proteins ». Journal of Virology 77, n^o 9 (1 mai 2003) : 5401–14. http://dx.doi.org/10.1128/jvi.77.9.5401-5414.2003.

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ABSTRACT Replication of the hepatitis C virus (HCV) genome has been proposed to take place close to the membrane of the endoplasmic reticulum in membrane-associated replicase complexes, as is the case with several other plus-strand RNA viruses, such as poliovirus and flaviviruses. The most obvious benefits of this property are the possibility of coupling functions residing in different polypeptidic chains and the sequestration of viral proteins and nucleic acids in a distinct cytoplasmic compartment with high local concentrations of viral components. Indeed, HCV nonstructural (NS) proteins were clearly colocalized in association with membranes derived from the endoplasmic reticulum. This observation, together with the demonstration of the existence of several physical interactions between HCV NS proteins, supports the idea of assembly of a highly ordered multisubunit protein complex(es) probably involved in the replication of the viral genome. The objective of this study, therefore, was to examine all potential interactions between HCV NS proteins which could result in the formation of a replication complex(es). We identified several interacting viral partners by using a glutathione S-transferase pull-down assay, by in vitro and ex vivo coimmunoprecipitation experiments in adenovirus-infected Huh-7 cells allowing the expression of HCV NS proteins, and, finally, by using the yeast two-hybrid system. In addition, by confocal laser scanning microscopy, NS proteins were clearly shown to colocalize when expressed together in Huh-7 cells. We have been able to demonstrate the existence of a complex network of interactions implicating all six NS proteins. Our observations confirm previously described associations and identify several novel homo- and heterodimerizations.

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22

Lu, T., M. Vandyke et M. Sawadogo. « Protein-Protein Interaction Studies Using Immobilized Oligohistidine Fusion Proteins ». Analytical Biochemistry 213, n^o 2 (septembre 1993) : 318–22. http://dx.doi.org/10.1006/abio.1993.1427.

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23

Win, Debora, Amanda Streeter, Yakira Jack et Julia R. Koeppe. « Protein-protein interactions of complement proteins C3 and CFH ». Biophysical Journal 123, n^o 3 (février 2024) : 476a. http://dx.doi.org/10.1016/j.bpj.2023.11.2889.

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24

Paul, Sanjoy, et Ravindra Venkatramani. « Dynamical Metrics to Fingerprint Proteins and Protein-Protein Interactions ». Biophysical Journal 118, n^o 3 (février 2020) : 306a. http://dx.doi.org/10.1016/j.bpj.2019.11.1730.

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25

Schaeffer, R. D., et V. Daggett. « Protein folds and protein folding ». Protein Engineering Design and Selection 24, n^o 1-2 (3 novembre 2010) : 11–19. http://dx.doi.org/10.1093/protein/gzq096.

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26

Gaines, J. C., S. Acebes, A. Virrueta, M. Butler, L. Regan et C. S. O'Hern. « Comparing side chain packing in soluble proteins, protein-protein interfaces, and transmembrane proteins ». Proteins : Structure, Function, and Bioinformatics 86, n^o 5 (26 février 2018) : 581–91. http://dx.doi.org/10.1002/prot.25479.

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27

Finkelstein, A. V. « Can protein unfolding simulate protein folding ? » Protein Engineering Design and Selection 10, n^o 8 (1 août 1997) : 843–45. http://dx.doi.org/10.1093/protein/10.8.843.

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28

Sear, Richard P. « Specific protein–protein binding in many-component mixtures of proteins ». Physical Biology 1, n^o 2 (29 avril 2004) : 53–60. http://dx.doi.org/10.1088/1478-3967/1/2/001.

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29

Huang, Hsien-Da, Tzong-Yi Lee, Li-Cheng Wu, Feng-Mao Lin, Hsueh-Fen Juan, Jorng-Tzong Horng et Ann-Ping Tsou. « MultiProtIdent : Identifying Proteins Using Database Search and Protein−Protein Interactions ». Journal of Proteome Research 4, n^o 3 (juin 2005) : 690–97. http://dx.doi.org/10.1021/pr0498335.

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30

Wadahama, Hiroyuki, Shinya Kamauchi, Masao Ishimoto, Teruo Kawada et Reiko Urade. « Protein disulfide isomerase family proteins involved in soybean protein biogenesis ». FEBS Journal 274, n^o 3 (20 décembre 2006) : 687–703. http://dx.doi.org/10.1111/j.1742-4658.2006.05613.x.

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31

LI, MIN, JIAN-XIN WANG, HUAN WANG et YI PAN. « IDENTIFICATION OF ESSENTIAL PROTEINS FROM WEIGHTED PROTEIN–PROTEIN INTERACTION NETWORKS ». Journal of Bioinformatics and Computational Biology 11, n^o 03 (juin 2013) : 1341002. http://dx.doi.org/10.1142/s0219720013410023.

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Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein–protein interactions has produced unprecedented opportunities for detecting protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. Unfortunately, the protein–protein interactions produced by high-throughput experiments generally have high false positives. Moreover, most of centrality measures based on network topology are sensitive to false positives. We therefore propose a new method for evaluating the confidence of each interaction based on the combination of logistic regression-based model and function similarity. Nine standard centrality measures in weighted network were redefined in this paper. The experimental results on a yeast protein interaction network shows that the weighting method improved the performance of centrality measures considerably. More essential proteins were discovered by the weighted centrality measures than by the original centrality measures used in the unweighted network. Even about 20% improvements were obtained from closeness centrality and subgraph centrality.

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32

Garapati, Hita Sony, Gurranna Male et Krishnaveni Mishra. « Predicting subcellular localization of proteins using protein-protein interaction data ». Genomics 112, n^o 3 (mai 2020) : 2361–68. http://dx.doi.org/10.1016/j.ygeno.2020.01.007.

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33

Zhang, Zhaopeng, Jishou Ruan, Jianzhao Gao et Fang-Xiang Wu. « Predicting essential proteins from protein-protein interactions using order statistics ». Journal of Theoretical Biology 480 (novembre 2019) : 274–83. http://dx.doi.org/10.1016/j.jtbi.2019.06.022.

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34

Meier, Matthias, Doron Gerber et Stephen Quake. « Functional Assignment of Hypothetical Proteins from Protein-Protein Interaction Networks ». Biophysical Journal 98, n^o 3 (janvier 2010) : 741a. http://dx.doi.org/10.1016/j.bpj.2009.12.4062.

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35

Vos, Michel J., Marianne P. Zijlstra, Serena Carra, Ody C. M. Sibon et Harm H. Kampinga. « Small heat shock proteins, protein degradation and protein aggregation diseases ». Autophagy 7, n^o 1 (janvier 2011) : 101–3. http://dx.doi.org/10.4161/auto.7.1.13935.

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36

Wilson, Bridget, Lance A. Liotta et Emanuel Petricoin III. « Monitoring Proteins and Protein Networks Using Reverse Phase Protein Arrays ». Disease Markers 28, n^o 4 (2010) : 225–32. http://dx.doi.org/10.1155/2010/240248.

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Recent advances in high throughput, high content “omic” technologies coupled with clinical information has lead to the expectation that the complexity of the molecular information generated will lead to more robust scientific research as well as the expectation that overarching therapeutic approaches will be patient-tailored to the underlying specific molecular defects of the disease. As disease understanding progresses and more therapeutics, which predominately target proteins, are developed there is a need to more confidently determine the protein signaling events that can be correlated with drug response since the deranged protein signaling networks are often the drug target itself. In this environment, the Reverse Phase Protein Microarray (RPMA) can be utilized to address the needs of both clinical screening and disease understanding through its ability to provide an unmatched functional and highly multiplexed signaling network level mapping of ongoing signaling activation, coupled with the ability of the platform to provide this information reproducibly from a tiny needle biopsy specimen or fine needle aspirate. This platform has now been utilized for biomarker discovery/validation and advancements in disease understanding both in the clinic and at the bench in the fields of cancer, liver disease, immunological disorders, and bacterial infection.

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37

Nchongboh, Chofong Gilbert, Guan-wei Wu, Ni Hong et Guo-ping Wang. « Protein–protein interactions between proteins of Citrus tristeza virus isolates ». Virus Genes 49, n^o 3 (27 juillet 2014) : 456–65. http://dx.doi.org/10.1007/s11262-014-1100-x.

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38

Koike, Manabu, Takashi Miyasaka, Tsuneyo Mimori et Tadahiro Shiomi. « Subcellular Localization and Protein-Protein Interaction Regions of Ku Proteins ». Biochemical and Biophysical Research Communications 252, n^o 3 (novembre 1998) : 679–85. http://dx.doi.org/10.1006/bbrc.1998.9368.

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39

Lin, Hening, et Virginia W. Cornish. « ChemInform Abstract : In vivo Protein-Protein Interaction Assays : Beyond Proteins ». ChemInform 32, n^o 21 (26 mai 2010) : no. http://dx.doi.org/10.1002/chin.200121275.

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40

Yadav, Keerti Kumar, et Ajay Kumar Singh. « Topology-based protein–protein interaction analysis of oral cancer proteins ». Current Science 123, n^o 10 (25 novembre 2022) : 1216. http://dx.doi.org/10.18520/cs/v123/i10/1216-1224.

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41

Vakser, IIya A. « Main-chain complementarity in protein-protein recognition ». "Protein Engineering, Design and Selection" 9, n^o 9 (1996) : 741–44. http://dx.doi.org/10.1093/protein/9.9.741.

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42

Lei, H., et Y. Duan. « Incorporating intermolecular distance into protein-protein docking ». Protein Engineering Design and Selection 17, n^o 12 (16 février 2005) : 837–45. http://dx.doi.org/10.1093/protein/gzh100.

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Abdullah, Syahid, Wisnu Ananta Kusuma et Sony Hartono Wijaya. « Sequence-based prediction of protein-protein interaction using autocorrelation features and machine learning ». Jurnal Teknologi dan Sistem Komputer 10, n^o 1 (4 janvier 2022) : 1–11. http://dx.doi.org/10.14710/jtsiskom.2021.13984.

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Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). The prediction models with the three autocorrelation methods can produce predictions with high average accuracy, which is 95.34% for Geary in KNN, 97.43% for Geary in RF, and 97.11% for Geary and Moran in SVM. In addition, the interacting protein pairs tend to have similar autocorrelation characteristics. Thus, the autocorrelation method can be used to predict PPI well.

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44

Diansyah, Mohammad Romano, Wisnu Ananta Kusuma et Annisa Annisa. « Identification of significant protein in protein-protein interaction of Alzheimer disease using top-k representative skyline query ». Jurnal Teknologi dan Sistem Komputer 9, n^o 3 (24 avril 2021) : 126–32. http://dx.doi.org/10.14710/jtsiskom.2021.13985.

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Alzheimer's disease is the most common neurodegenerative disease. This study aims to analyze protein-protein interaction (PPI) to provide a better understanding of multifactorial neurodegenerative diseases and can be used to find proteins that have a significant role in Alzheimer's disease. PPI data were obtained from experimental and computational predictions and analyzed using centrality measures. The Top-k RSP method was applied to find significant proteins in PPI networks using the dominance rule. The method was applied to the PPI data with the interaction sources from the experimental and experiment+prediction. The results indicate that APP and PSEN1 are significant proteins for Alzheimer's disease. This study also showed that both data sources (experiment+prediction) and the Top-k RSP algorithm proved useful for PPI analysis of Alzheimer's disease.

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El Hefnawi, Mahmoud M., Mohamed E. Hasan, Amal Mahmoud, Yehia A. Khidr, Wessam H. El Behaidy, El-sayed A. El-absawy et Alaa A. Hemeida. « Prediction and Analysis of Three-Dimensional Structure of the p7- Transactivated Protein1 of Hepatitis C Virus ». Infectious Disorders - Drug Targets 19, n^o 1 (4 février 2019) : 55–66. http://dx.doi.org/10.2174/1871526518666171215123214.

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Background:The p7-transactivated protein1 of Hepatitis C virus is a small integral membrane protein of 127 amino acids, which is crucial for assembly and release of infectious virions. Ab initio or comparative modelling, is an essential tool to solve the problem of protein structure prediction and to comprehend the physicochemical fundamental of how proteins fold in nature.Results:Only one domain (1-127) of p7-transactivated protein1 has been predicted using the systematic in silico approach, ThreaDom. I-TASSER was ranked as the best server for full-length 3-D protein structural predictions of p7-transactivated protein1 where the benchmarked scoring system such as C-score, TM-score, RMSD and Z-score are used to obtain quantitative assessments of the I-TASSER models. Scanning protein motif databases, along with secondary and surface accessibility predictions integrated with post translational modification sites (PTMs) prediction revealed functional and protein binding motifs. Three protein binding motifs (two Asp/Glutamnse, CTNNB1- bd_N) with high sequence conservation and two PTMs prediction: Camp_phospho_site and Myristyl site were predicted using BLOCKS and PROSITE scan. These motifs and PTMs were related to the function of p7-transactivated protein1 protein in inducing ion channel/pore and release of infectious virions. Using SCOP, only one hit matched protein sequence at 71-120 was classified as small proteins and FYVE/PHD zinc finger superfamily.Conclusion:Integrating this information about the p7-transactivated protein1 with SCOP and CATH annotations of the templates facilitates the assignment of structure–function/ evolution relationships to the known and the newly determined protein structures.

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46

Смирнова, Ирина, Irina Smirnova, Николай Гутов, Nikolay Gutov, Андрей Лукин et Andrey Lukin. « Research of composition of milk protein concentrates ». Food Processing : Techniques and Technology 48, n^o 1 (10 janvier 2019) : 85–90. http://dx.doi.org/10.21603/2074-9414-2018-1-85-90.

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Emergence of the dairy products enriched with milky proteinaceous concentrates is connected with low level of consumption of protein the population. Results of a research of structure of two samples of milk protein concentrates – Promilk 852 FBI and Promilk Kappa Optimum for the purpose of their further application in production of dairy products are presented in article. Fractions of proteins of milk protein concentrates with use of size of molecular weight are defined. As a result of electrophoretic division of fractions of proteins the method of a free electrophoresis by means of a cell for an electrophoresis of MINI-PROTEAN has received an initial electrophoregram. In the studied samples the number of fractions of serumal proteins and casein is identified. Absolute values of fractions of serumal proteins and casein in samples of milk protein concentrates are calculated. On the basis of the received values of fractions of serumal proteins and casein their percentage in milk protein concentrates is determined. The received results allow to draw a conclusion that the studied samples of milk protein concentrates can be used in production of dairy products as an additional component for increase in nutrition value of a ready-made product.

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HAO, Liyang, Quan PAN et Shaowu ZHANG. « Prediction of Drug-Target Proteins by Integrating Protein-Protein Interaction Network and Protein Sequence Similarity ». Acta Biophysica Sinica 29, n^o 9 (2013) : 695. http://dx.doi.org/10.3724/sp.j.1260.2013.30042.

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Zhang, Changsheng, Bo Tang, Qian Wang et Luhua Lai. « Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening ». Proteins : Structure, Function, and Bioinformatics 82, n^o 10 (3 juin 2014) : 2472–82. http://dx.doi.org/10.1002/prot.24611.

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Sawyer, Nicholas, Danielle M. Williams et Lynne Regan. « Protein goldendoodles : Designing new proteins ». Biochemist 36, n^o 1 (1 février 2014) : 28–33. http://dx.doi.org/10.1042/bio03601028.

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The goldendoodle (Figure 1) is a breed of dog created to combine the desirable features of the golden retriever (calm personality, good with people and an excellent service dog) with those of the poodle (low shedding and hypoallergenic). The result surpasses expectations: not only does the goldendoodle have a great personality and low shedding, but also the animal is exceedingly cute and in great demand. Protein design, the creation of novel proteins either de novo or by extensive mutagenesis of natural proteins, has likewise produced many ‘goldendoodle-esque’ proteins whose unprecedented combination of stability and function have revolutionized academic and clinical research. Here, we discuss the history of protein design and highlight some particularly successful protein designs of this type.

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Vershon, Andrew K. « Protein interactions of homeodomain proteins ». Current Opinion in Biotechnology 7, n^o 4 (août 1996) : 392–96. http://dx.doi.org/10.1016/s0958-1669(96)80113-3.

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